Ideal levels of green space may differ depending on density. At the global scale, there is a need to describe greenness appropriate for its region and population-density context. Such knowledge would identify feasible target areas for improved greening at the local level allowing to estimate the health benefits of such scenarios.
Estimate the health impacts of urban green scenarios based on population density-stratified measures of greenness in cities around the world.
Stratify by biome, then by city, then by Landscan population category and then:
Measure tertiles of NDVI in each biome-city-pop-group stratum.
Scenario:
Set the NDVI of pixels in the bottom two tertiles to the NDVI value of the 83rd percentile (median of top tertile). In other words, only intervene upon pixels in the bottom two tertiles for that population category for that city for that biome. Note there are a few cities where biome varies within city.
The idea is that this would be a realistic intervention given the target NDVI is relative to the biome, city, and population density category.
Conduct HIA using mean of Landscan populatoin values in that category (also plan to use min/max for uncertainty analyses).
HIA analysis complete for continental USA (48 states+DC) following those steps.
Working on expanding to global.
Discussion question: consider restricting to cities above a certain population?
The map visualizes values categories coded 1-8 for easier visualization. The corresponding population categories appear in the table below.
## # A tibble: 9 × 4
## pop_cat_1_8 pop_cat_min_val pop_cat_max_val pop_cat_mean_val
## <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0
## 2 1 1 5 3
## 3 2 6 25 15.5
## 4 3 26 50 38
## 5 4 51 100 75.5
## 6 5 101 500 300.
## 7 6 501 2500 1500.
## 8 7 2501 5000 3750.
## 9 8 5001 185000 95000.Data are large, so only mapping Colorado. Visualize the area (square kilometers) of urban boundaries.
| biome name imp | pop cat mean val scaled | pop cat min val scaled | pop cat max val scaled | ndvi 2019 mean | ndvi 2019 sd | ndvi diff mean | deaths baseline mean | deaths baseline min | deaths baseline max | deaths prevented mean | deaths prevented min | deaths prevented max | deaths prevented per 1k pop mean | deaths prevented per 1k pop min | deaths prevented per 1k pop max |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Deserts & Xeric Shrublands | 18,663,810 | 5,291,608.8 | 32,036,011 | 0.36 | 0 | 0 | 204,519.661 | 57,986 | 351,053 | 4,876.72 | 1,446.31 | 8,307.12 | 0 | 0 | 0.259 |
| Flooded Grasslands & Savannas | 8,635,230 | 2,051,279.3 | 15,219,180 | 0.58 | 0 | 0 | 94,625.599 | 22,478 | 166,773 | 3,827.79 | 731.49 | 6,924.09 | 0 | 0 | 0.455 |
| Mangroves | 85,168 | 29,658.1 | 140,677 | 0.67 | 0 | 0 | 933.274 | 325 | 1,542 | 16.64 | 5.58 | 27.71 | 0 | 0 | 0.197 |
| Mediterranean Forests, Woodlands & Scrub | 55,446,619 | 11,291,656.5 | 99,601,582 | 0.47 | 0 | 0 | 607,588.893 | 123,735 | 1,091,443 | 25,041.28 | 4,638.83 | 45,443.73 | 0 | 0 | 0.456 |
| Temperate Broadleaf & Mixed Forests | 150,389,494 | 32,467,659.1 | 268,311,329 | 0.72 | 0 | 0 | 1,647,981.202 | 355,783 | 2,940,179 | 57,880.88 | 10,179.02 | 105,582.73 | 0 | 0 | 0.394 |
| Temperate Conifer Forests | 15,525,499 | 4,349,817.3 | 26,701,182 | 0.68 | 0 | 0 | 170,129.778 | 47,666 | 292,594 | 4,998.94 | 1,313.66 | 8,684.23 | 0 | 0 | 0.325 |
| Temperate Grasslands, Savannas & Shrublands | 120,739,102 | 32,153,112.2 | 209,325,092 | 0.66 | 0 | 0 | 1,323,069.620 | 352,337 | 2,293,803 | 40,784.45 | 9,814.30 | 71,754.59 | 0 | 0 | 0.343 |
| Tropical & Subtropical Grasslands, Savannas & Shrublands | 10,619,909 | 2,901,686.3 | 18,338,133 | 0.60 | 0 | 0 | 116,373.895 | 31,797 | 200,951 | 3,752.49 | 957.26 | 6,547.71 | 0 | 0 | 0.357 |
Cities above 1,000,000 people (per Landscan) sorted ascending by deaths prevented per 1k pop (top 10)
## # A tibble: 100 × 17
## ORIG_FID city_name pop_c…¹ pop_c…² pop_c…³ death…⁴ death…⁵ death…⁶ death…⁷
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 54733 <NA> 1.68e3 6.04e2 2.76e3 1.85e1 6.62e0 3.03e1 1.36e+0
## 2 60310 New York Ci… 1.26e7 9.17e5 2.43e7 1.38e5 1.01e4 2.66e5 9.50e+3
## 3 60228 Dayton 4.49e2 1.51e2 7.47e2 4.92e0 1.65e0 8.19e0 3.10e-1
## 4 53567 Yuma 7.35e4 2.64e4 1.21e5 8.06e2 2.90e2 1.32e3 4.94e+1
## 5 53578 El Centro 3.73e4 1.71e4 5.75e4 4.09e2 1.88e2 6.30e2 2.33e+1
## 6 61415 Reading 4.09e5 8.99e4 7.28e5 4.48e3 9.85e2 7.98e3 2.50e+2
## 7 53575 Brawley 1.98e4 7.56e3 3.21e4 2.17e2 8.28e1 3.52e2 1.21e+1
## 8 53570 Blythe 9.42e3 3.15e3 1.57e4 1.03e2 3.45e1 1.72e2 5.74e+0
## 9 53947 San Jose 1.23e7 2.37e6 2.23e7 1.35e5 2.59e4 2.44e5 6.87e+3
## 10 55562 Council Blu… 7.01e4 2.38e4 1.16e5 7.68e2 2.60e2 1.28e3 3.85e+1
## # … with 90 more rows, 8 more variables: deaths_prevented_min <dbl>,
## # deaths_prevented_max <dbl>, ndvi_2019_mean <dbl>, ndvi_2019_sd <dbl>,
## # ndvi_diff_mean <dbl>, deaths_prevented_per_1k_pop_mean <dbl>,
## # deaths_prevented_per_1k_pop_min <dbl>,
## # deaths_prevented_per_1k_pop_max <dbl>, and abbreviated variable names
## # ¹pop_cat_mean_val_scaled, ²pop_cat_min_val_scaled, ³pop_cat_max_val_scaled,
## # ⁴deaths_baseline_mean, ⁵deaths_baseline_min, ⁶deaths_baseline_max, …Results are presented at the level of the global urban boundary. Following the methods described above, they are first stratified by biome and are thus relative to biome within urban boundary if biome varies within urban boundary.